Semantic Proximity Search on Graphswith Metagraph-based Learning

Yuan Fang1 Wenqing Lin1 Vincent Zheng2 Min Wu1 Kevin Chang23 Xiao-Li Li1

Problem: Semantic Proximity Search on Heterogeneous Graph

Insights: Metagraphs to “Explain” Different Semantic Classes

1 Institute for Infocomm Research, Singapore2 Advanced Digital Sciences Center, Singapore3 University of Illinois at Urbana-Champaign, USA

Object/Attribute

TypeWhich users are close to /related to Bob?

Family? (Alice)Classmates? (Tom)

On a “typed” object graph that captures users and their attributes on a social network:

Family[Bob & Alice]

Classmates[Kate & Jay, Bob & Tom]

Close friends[Kate & Alice] [Kate & Jay]

Offline

Online

mining metagraphs

matching metagraphs (ie,

finding instances)indexing

training

testing

Definition of Proximity Basic Learning Model

Training

Proximity of two nodes on graph

: weight for metagraph

i : # times , co-occur in instances of metagraphi : # times occurs in instances of metagraph

Each example is a triplet: for query , is ranked before y.

Pairwise learning to rank

Objective function

Dual-Stage Training

Expensive to process/match all metagraphs Yet not all metagraphs are useful

identify seed metagraphs

learn with seed metagraphs

re-learn with seed + selected

metagraphs

select more metagraphs

based on weights of seed metagraphs and their structural relationship with other metagraphs

Ove

rall

Fra

mew

ork

Matching Metagraphs

Existing method

Symmetry-based matching

o Backtracking DFS searcho Node by node until an entire

matched instance is foundo Fail to leverage symmetric

components

o Many metagraphs are symmetrico Avoid redundant computation

Main Results

Datasets: • College & Coworkers (labelled on LinkedIn)• Family & Classmate (rule on Facebook)

Baselines:• MGP: metagraph-based proximity (ours)• MPP: metapath-based proximity• MGP-U: all metagraphs have uniform weights• MGP-B: only use the best metagraph• SRW: supervised random walk